The Efficiency of Dynamic and Static Facial Expression Recognition

Overview

Journal J Vis

Specialty Ophthalmology

Date 2013 Apr 27

PMID 23620533

Citations 18

Authors

Jason M Gold

Jarrett D Barker

Shawn Barr

Jennifer L Bittner

W Drew Bromfield

Nicole Chu

Roy A Goode

Doori Lee

Michael Simmons

Aparna Srinath

Affiliations

Soon will be listed here.

Abstract

Unlike frozen snapshots of facial expressions that we often see in photographs, natural facial expressions are dynamic events that unfold in a particular fashion over time. But how important are the temporal properties of expressions for our ability to reliably extract information about a person's emotional state? We addressed this question experimentally by gauging human performance in recognizing facial expressions with varying temporal properties relative to that of a statistically optimal ("ideal") observer. We found that people recognized emotions just as efficiently when viewing them as naturally evolving dynamic events, temporally reversed events, temporally randomized events, or single images frozen in time. Our results suggest that the dynamic properties of human facial movements may play a surprisingly small role in people's ability to infer the emotional states of others from their facial expressions.

Citing Articles

Expression perceptive fields explain individual differences in the recognition of facial emotions.

Murray T, Binetti N, Venkataramaiyer R, Namboodiri V, Cosker D, Viding E Commun Psychol. 2024; 2(1):62.

PMID: 39242751 PMC: 11332168. DOI: 10.1038/s44271-024-00111-7.

Idiosyncratic fixation patterns generalize across dynamic and static facial expression recognition.

Paparelli A, Sokhn N, Stacchi L, Coutrot A, Richoz A, Caldara R Sci Rep. 2024; 14(1):16193.

PMID: 39003314 PMC: 11246522. DOI: 10.1038/s41598-024-66619-4.

Recognizing facial expressions of emotion amid noise: A dynamic advantage.

Richoz A, Stacchi L, Schaller P, Lao J, Papinutto M, Ticcinelli V J Vis. 2024; 24(1):7.

PMID: 38197738 PMC: 10790674. DOI: 10.1167/jov.24.1.7.

Human and machine recognition of dynamic and static facial expressions: prototypicality, ambiguity, and complexity.

Kim H, Kuster D, Girard J, Krumhuber E Front Psychol. 2023; 14:1221081.

PMID: 37794914 PMC: 10546417. DOI: 10.3389/fpsyg.2023.1221081.

FER-PCVT: Facial Expression Recognition with Patch-Convolutional Vision Transformer for Stroke Patients.

Fan Y, Wang H, Zhu X, Cao X, Yi C, Chen Y Brain Sci. 2022; 12(12).

PMID: 36552086 PMC: 9776282. DOI: 10.3390/brainsci12121626.

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